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Record W2053955061 · doi:10.1175/jcli-d-13-00535.1

Radiative Forcing of Quadrupling CO2

2013· article· en· W2053955061 on OpenAlexafffund
Minghong Zhang, Yi Huang

Bibliographic record

VenueJournal of Climate · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsForcing (mathematics)Radiative forcingEnvironmental scienceCloud forcingClimatologyAtmospheric sciencesRadiative transferClimate modelCoupled model intercomparison projectAtmosphere (unit)Cloud feedbackMeteorologyPhysicsClimate changeClimate sensitivityAerosolGeology

Abstract

fetched live from OpenAlex

Abstract An analysis method proposed by Huang is improved and used to dissect the radiative forcing in the instantaneous quadrupling CO2 experiment from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Multiple validation tests show that the errors in the forcing estimates are generally within 10%. The results show that quadrupling CO2, on average, induces a global-mean all-sky instantaneous top-of-the-atmosphere forcing of 5.4 W m−2, which is amended by a stratospheric adjustment of 1.9 W m−2 and a tropospheric adjustment of −0.1 W m−2. The resulting fully adjusted radiative forcing has an ensemble mean of 7.2 W m−2 and a substantial intermodel spread (maximum–minimum) of 2.4 W m−2, which results from all the forcing components, especially the instantaneous forcing and tropospheric adjustment. The fidelity of the linear decomposition of the overall radiation variation is improved when forcing is explicitly estimated for each model. A significant contribution by forcing uncertainty to the intermodel spread of the surface temperature projection is verified. The results reaffirm the importance of evaluating the radiative forcing components in climate feedback analyses.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.382
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.213
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations69
Published2013
Admission routes2
Has abstractyes

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